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4Adaptive Learning Rates for Support Vector Machines Working on Data with Low Intrinsic Dimension
T. Hamm, and I. Steinwart. Ann. Statist., (2021)https://arxiv.org/abs/2003.06202.
3 years ago by @mathematik
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8liquidSVM: A Fast and Versatile SVM Package
I. Steinwart, and P. Thomann. Fakultät für Mathematik und Physik, Universität Stuttgart, (2017)
3 years ago by @mathematik
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4Learning with Hierarchical Gaussian Kernels
I. Steinwart, P. Thomann, and N. Schmid. Fakultät für Mathematik und Physik, Universität Stuttgart, (2016)
3 years ago by @mathematik
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2Intrinsic Dimension Adaptive Partitioning for Kernel Methods
T. Hamm, and I. Steinwart. Fakultät für Mathematik und Physik, Universität Stuttgart, (2021)
3 years ago by @mathematik
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4Adaptive Learning Rates for Support Vector Machines Working on Data with Low Intrinsic Dimension
T. Hamm, and I. Steinwart. Ann. Statist., (2021)https://arxiv.org/abs/2003.06202.
3 years ago by @mathematik
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3Optimal Learning with Anisotropic Gaussian SVMs
H. Hang, and I. Steinwart. Appl. Comput. Harmon. Anal., (2021)https://arxiv.org/abs/1810.02321.
3 years ago by @mathematik
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4Optimal Rates for Regularized Least Squares Regression
I. Steinwart, D. Hush, and C. Scovel. Proceedings of the 22nd Annual Conference on Learning Theory, page 79--93. (2009)http://www.cs.mcgill.ca/~colt2009/papers/038.pdf.
3 years ago by @mathematik
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4Robust learning from bites for data mining
A. Christmann, I. Steinwart, and M. Hubert. Comput. Statist. Data Anal., (2007)
3 years ago by @mathematik
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3Fast rates for support vector machines using Gaussian kernels
I. Steinwart, and C. Scovel. Ann. Statist., (2007)http://arxiv.org/pdf/0708.1838.
3 years ago by @mathematik
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4Some Remarks on the Statistical Analysis of SVMs and Related Methods
I. Steinwart. Empirical Inference -- Festschrift in Honor of Vladimir N. Vapnik, chapter 4, Springer, Berlin, (2013)
3 years ago by @mathematik
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4Stability of unstable learning algorithms
D. Hush, C. Scovel, and I. Steinwart. Mach. Learn., (2007)
3 years ago by @mathematik
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3Sparseness of support vector machines---some asymptotically sharp bounds
I. Steinwart. Advances in Neural Information Processing Systems 16, page 1069--1076. Cambridge, MA, MIT Press, (2004)http://books.nips.cc/papers/files/nips16/NIPS2003_LT01.pdf.
3 years ago by @mathematik
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2On the optimal parameter choice for $\nu$-support vector machines
I. Steinwart. IEEE Transactions on Pattern Analysis and Machine Intelligence, (2003)
3 years ago by @mathematik
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3Sparsity of SVMs that use the $\epsilon$-insensitive loss
I. Steinwart, and A. Christmann. Advances in Neural Information Processing Systems 21, page 1569--1576. (2009)http://books.nips.cc/papers/files/nips21/NIPS2008_0274.pdf.
3 years ago by @mathematik
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3Support vector machines are universally consistent
I. Steinwart. J. Complexity, (2002)
3 years ago by @mathematik
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3Optimal regression rates for SVMs using Gaussian kernels
M. Eberts, and I. Steinwart. Electron. J. Stat., (2013)
3 years ago by @mathematik
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4Optimal Learning Rates for Localized SVMs
M. Meister, and I. Steinwart. J. Mach. Learn. Res., (2016)http://www.jmlr.org/papers/volume17/14-023/14-023.pdf.
3 years ago by @mathematik
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2Consistency of support vector machines and other regularized kernel machines
I. Steinwart. IEEE Trans. Inform. Theory, (2005)
3 years ago by @mathematik
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4Support Vector Machines
I. Steinwart, and A. Christmann. Springer, New York, (2008)
3 years ago by @mathematik
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3Consistency and robustness of kernel-based regression in convex risk minimization
A. Christmann, and I. Steinwart. Bernoulli, (2007)http://arxiv.org/pdf/0709.0626.
3 years ago by @mathematik
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publications (hide)42
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4Adaptive Learning Rates for Support Vector Machines Working on Data with Low Intrinsic Dimension

8liquidSVM: A Fast and Versatile SVM Package

4Learning with Hierarchical Gaussian Kernels

2Intrinsic Dimension Adaptive Partitioning for Kernel Methods

4Adaptive Learning Rates for Support Vector Machines Working on Data with Low Intrinsic Dimension

3Optimal Learning with Anisotropic Gaussian SVMs

4Optimal Rates for Regularized Least Squares Regression

4Robust learning from bites for data mining

3Fast rates for support vector machines using Gaussian kernels

4Some Remarks on the Statistical Analysis of SVMs and Related Methods

4Stability of unstable learning algorithms

3Sparseness of support vector machines---some asymptotically sharp bounds

2On the optimal parameter choice for $\nu$-support vector machines

3Sparsity of SVMs that use the $\epsilon$-insensitive loss

3Support vector machines are universally consistent

3Optimal regression rates for SVMs using Gaussian kernels

4Optimal Learning Rates for Localized SVMs

2Consistency of support vector machines and other regularized kernel machines

4Support Vector Machines

3Consistency and robustness of kernel-based regression in convex risk minimization

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